marcingrzegzhik t1_j6aic0m wrote

If you are looking for product-query similarity, you could try using a Word2Vec model. You can train a Word2Vec model on your dataset, and then use the model to find the most similar words for each product title and user query. This should give you a better understanding of the similarity between the two.

You can also try using an embedding-based approach, such as using an embedding layer in a neural network. This would enable you to learn more complex relationships between product titles and user queries.

You could also try using a matrix factorization technique such as Singular Value Decomposition (SVD) or Non-Negative Matrix Factorization (NMF). These methods can help you to identify latent features in your dataset, which can be used to generate better recommendations.

Hope this helps!


marcingrzegzhik t1_j69k6qd wrote

It's definitely a valid interview question, but it's not something you should be asked to do during a live call. It's too much to tackle in the limited time of a call and it's not a fair way to assess your skills. I would suggest asking to review a code sample you've written in the past that demonstrates your knowledge and experience. That would be a better way to assess your skills and it would be much less stressful. Good luck!


marcingrzegzhik t1_j67whko wrote

Hi there!

I have trained ImageNet several times myself, using both local and cloud resources.

I would recommend starting with a tutorial on how to get it running locally - there are many out there. As for the cloud resources, I found Google Cloud to be the best option in terms of cost/performance. In terms of expenses, the cost of training on ImageNet can be quite high, depending on the resources you use. As for the model, I would recommend running the model at least three times, so that you can get an accurate estimate of the performance. As for early stopping, I would recommend using the validation set - this will give you a more accurate representation of the model's performance.

Hope this helps!


marcingrzegzhik t1_j67vr5l wrote

It depends on the scope of your projects. If you're only training small models (like GANs, CNNs, etc.), then a decent modern laptop with 8+ GB RAM and an intel i7 or Ryzen 7 processor should suffice. GPUs are nice to have, but with an Intel i7 or Ryzen 7 processor you can do most of the work without them. As for the OS, Windows and Linux are both fine, but I'd recommend Linux for ML projects for maximum compatibility. Hope this helps!


marcingrzegzhik t1_j67tza8 wrote

No, the license does not mean you cannot use the ideas from the paper in a commercial product. It just means that you cannot use the work itself or any derivative works for commercial purposes. However, you can use the ideas from the paper, as long as you don’t directly copy or use any of the code/materials from the paper. To be safe, you should also make sure that you don’t infringe on any patents associated with the paper.


marcingrzegzhik t1_j67s9fp wrote

Forward-forward learning is a very interesting concept, and I think that in some cases it could definitely yield better results than distributed learning with backprop. It really depends on the size of the model, the latency of the connection, and the bandwidth of the slowest machine. I'm sure that in some cases it could be much faster, but I'm curious to know if there are any other advantages to using forward-forward learning over backprop for distributed learning.